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import os
from functools import partial
from typing import List
import sentencepiece as spm
import torch
import torchaudio
from common import (
Batch,
batch_by_token_count,
FunctionalModule,
GlobalStatsNormalization,
piecewise_linear_log,
post_process_hypos,
spectrogram_transform,
WarmupLR,
)
from pytorch_lightning import LightningModule
from torchaudio.models import emformer_rnnt_base, RNNTBeamSearch
class CustomDataset(torch.utils.data.Dataset):
r"""Sort LibriSpeech samples by target length and batch to max token count."""
def __init__(self, base_dataset, max_token_limit):
super().__init__()
self.base_dataset = base_dataset
fileid_to_target_length = {}
idx_target_lengths = [
(idx, self._target_length(fileid, fileid_to_target_length))
for idx, fileid in enumerate(self.base_dataset._walker)
]
assert len(idx_target_lengths) > 0
idx_target_lengths = sorted(idx_target_lengths, key=lambda x: x[1], reverse=True)
assert max_token_limit >= idx_target_lengths[0][1]
self.batches = batch_by_token_count(idx_target_lengths, max_token_limit)
def _target_length(self, fileid, fileid_to_target_length):
if fileid not in fileid_to_target_length:
speaker_id, chapter_id, _ = fileid.split("-")
file_text = speaker_id + "-" + chapter_id + self.base_dataset._ext_txt
file_text = os.path.join(self.base_dataset._path, speaker_id, chapter_id, file_text)
with open(file_text) as ft:
for line in ft:
fileid_text, transcript = line.strip().split(" ", 1)
fileid_to_target_length[fileid_text] = len(transcript)
return fileid_to_target_length[fileid]
def __getitem__(self, idx):
return [self.base_dataset[subidx] for subidx in self.batches[idx]]
def __len__(self):
return len(self.batches)
class LibriSpeechRNNTModule(LightningModule):
def __init__(
self,
*,
librispeech_path: str,
sp_model_path: str,
global_stats_path: str,
):
super().__init__()
self.model = emformer_rnnt_base(num_symbols=4097)
self.loss = torchaudio.transforms.RNNTLoss(reduction="sum", clamp=1.0)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=5e-4, betas=(0.9, 0.999), eps=1e-8)
self.warmup_lr_scheduler = WarmupLR(self.optimizer, 10000)
self.train_data_pipeline = torch.nn.Sequential(
FunctionalModule(piecewise_linear_log),
GlobalStatsNormalization(global_stats_path),
FunctionalModule(partial(torch.transpose, dim0=1, dim1=2)),
torchaudio.transforms.FrequencyMasking(27),
torchaudio.transforms.FrequencyMasking(27),
torchaudio.transforms.TimeMasking(100, p=0.2),
torchaudio.transforms.TimeMasking(100, p=0.2),
FunctionalModule(partial(torch.nn.functional.pad, pad=(0, 4))),
FunctionalModule(partial(torch.transpose, dim0=1, dim1=2)),
)
self.valid_data_pipeline = torch.nn.Sequential(
FunctionalModule(piecewise_linear_log),
GlobalStatsNormalization(global_stats_path),
FunctionalModule(partial(torch.transpose, dim0=1, dim1=2)),
FunctionalModule(partial(torch.nn.functional.pad, pad=(0, 4))),
FunctionalModule(partial(torch.transpose, dim0=1, dim1=2)),
)
self.librispeech_path = librispeech_path
self.sp_model = spm.SentencePieceProcessor(model_file=sp_model_path)
self.blank_idx = self.sp_model.get_piece_size()
def _extract_labels(self, samples: List):
targets = [self.sp_model.encode(sample[2].lower()) for sample in samples]
lengths = torch.tensor([len(elem) for elem in targets]).to(dtype=torch.int32)
targets = torch.nn.utils.rnn.pad_sequence(
[torch.tensor(elem) for elem in targets],
batch_first=True,
padding_value=1.0,
).to(dtype=torch.int32)
return targets, lengths
def _train_extract_features(self, samples: List):
mel_features = [spectrogram_transform(sample[0].squeeze()).transpose(1, 0) for sample in samples]
features = torch.nn.utils.rnn.pad_sequence(mel_features, batch_first=True)
features = self.train_data_pipeline(features)
lengths = torch.tensor([elem.shape[0] for elem in mel_features], dtype=torch.int32)
return features, lengths
def _valid_extract_features(self, samples: List):
mel_features = [spectrogram_transform(sample[0].squeeze()).transpose(1, 0) for sample in samples]
features = torch.nn.utils.rnn.pad_sequence(mel_features, batch_first=True)
features = self.valid_data_pipeline(features)
lengths = torch.tensor([elem.shape[0] for elem in mel_features], dtype=torch.int32)
return features, lengths
def _train_collate_fn(self, samples: List):
features, feature_lengths = self._train_extract_features(samples)
targets, target_lengths = self._extract_labels(samples)
return Batch(features, feature_lengths, targets, target_lengths)
def _valid_collate_fn(self, samples: List):
features, feature_lengths = self._valid_extract_features(samples)
targets, target_lengths = self._extract_labels(samples)
return Batch(features, feature_lengths, targets, target_lengths)
def _test_collate_fn(self, samples: List):
return self._valid_collate_fn(samples), [sample[2] for sample in samples]
def _step(self, batch, batch_idx, step_type):
if batch is None:
return None
prepended_targets = batch.targets.new_empty([batch.targets.size(0), batch.targets.size(1) + 1])
prepended_targets[:, 1:] = batch.targets
prepended_targets[:, 0] = self.blank_idx
prepended_target_lengths = batch.target_lengths + 1
output, src_lengths, _, _ = self.model(
batch.features,
batch.feature_lengths,
prepended_targets,
prepended_target_lengths,
)
loss = self.loss(output, batch.targets, src_lengths, batch.target_lengths)
self.log(f"Losses/{step_type}_loss", loss, on_step=True, on_epoch=True)
return loss
def configure_optimizers(self):
return (
[self.optimizer],
[
{"scheduler": self.warmup_lr_scheduler, "interval": "step"},
],
)
def forward(self, batch: Batch):
decoder = RNNTBeamSearch(self.model, self.blank_idx)
hypotheses = decoder(batch.features.to(self.device), batch.feature_lengths.to(self.device), 20)
return post_process_hypos(hypotheses, self.sp_model)[0][0]
def training_step(self, batch: Batch, batch_idx):
return self._step(batch, batch_idx, "train")
def validation_step(self, batch, batch_idx):
return self._step(batch, batch_idx, "val")
def test_step(self, batch_tuple, batch_idx):
return self._step(batch_tuple[0], batch_idx, "test")
def train_dataloader(self):
dataset = torch.utils.data.ConcatDataset(
[
CustomDataset(
torchaudio.datasets.LIBRISPEECH(self.librispeech_path, url="train-clean-360"),
1000,
),
CustomDataset(
torchaudio.datasets.LIBRISPEECH(self.librispeech_path, url="train-clean-100"),
1000,
),
CustomDataset(
torchaudio.datasets.LIBRISPEECH(self.librispeech_path, url="train-other-500"),
1000,
),
]
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=None,
collate_fn=self._train_collate_fn,
num_workers=10,
shuffle=True,
)
return dataloader
def val_dataloader(self):
dataset = torch.utils.data.ConcatDataset(
[
CustomDataset(
torchaudio.datasets.LIBRISPEECH(self.librispeech_path, url="dev-clean"),
1000,
),
CustomDataset(
torchaudio.datasets.LIBRISPEECH(self.librispeech_path, url="dev-other"),
1000,
),
]
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=None,
collate_fn=self._valid_collate_fn,
num_workers=10,
)
return dataloader
def test_dataloader(self):
dataset = torchaudio.datasets.LIBRISPEECH(self.librispeech_path, url="test-clean")
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, collate_fn=self._test_collate_fn)
return dataloader
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